Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 520 412 372 656 750 757 633 753 896 554 844 114 436 854 871 473 183  39 227 590

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 896 436 633 227 854 473  NA 590 114 750  39 183 757 372 412 656 844 753  NA  NA 554 871 520

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 3 1 2 1 2 3 1 4 5 5

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
[26] "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y"
[26] "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "a" "g" "s" "e" "t" "L" "C" "K" "O" "X"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

integer(0)

which( manyNumbersWithNA > 900 )

integer(0)

which( is.na( manyNumbersWithNA ) )

[1]  7 19 20

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

integer(0)

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

integer(0)

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

integer(0)

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "L" "C" "K" "O" "X"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "a" "g" "s" "e" "t"

manyNumbers %in% 300:600

 [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE
[18] FALSE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  1  2  3 10 13 16 20

sum( manyNumbers %in% 300:600 )

[1] 7

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "large" "small" "large" "small" "large" "small" NA      "large" "small" "large" "small" "small"
[13] "large" "small" "small" "large" "large" "large" NA      NA      "large" "large" "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "large"   "small"   "large"   "small"   "large"   "small"   "UNKNOWN" "large"   "small"   "large"  
[11] "small"   "small"   "large"   "small"   "small"   "large"   "large"   "large"   "UNKNOWN" "UNKNOWN"
[21] "large"   "large"   "large"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1] 896   0 633   0 854   0  NA 590   0 750   0   0 757   0   0 656 844 753  NA  NA 554 871 520

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 3 1 2 4 5

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  3  1  2  4  5

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 1

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 896

which.min( manyNumbersWithNA )

[1] 11

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 39

range( manyNumbersWithNA, na.rm = TRUE )

[1]  39 896

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 896 436 633 227 854 473  NA 590 114 750  39 183 757 372 412 656 844 753  NA  NA 554 871 520

sort( manyNumbersWithNA )

 [1]  39 114 183 227 372 412 436 473 520 554 590 633 656 750 753 757 844 854 871 896

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  39 114 183 227 372 412 436 473 520 554 590 633 656 750 753 757 844 854 871 896  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 896 871 854 844 757 753 750 656 633 590 554 520 473 436 412 372 227 183 114  39  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 896 436 633 227 854

order( manyNumbersWithNA[1:5] )

[1] 4 2 3 5 1

rank( manyNumbersWithNA[1:5] )

[1] 5 2 3 1 4

sort( mixedLetters )

 [1] "a" "C" "e" "g" "K" "L" "O" "s" "t" "X"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 5.0 5.0 7.5 9.5 9.5 7.5 2.5 1.0 5.0 2.5

rank( manyDuplicates, ties.method = "min" )

 [1] 4 4 7 9 9 7 2 1 4 2

rank( manyDuplicates, ties.method = "random" )

 [1]  4  5  7  9 10  8  2  1  6  3

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000 -0.7471214  1.0710889 -0.5715681 -0.7874737
[10] -1.0968273  1.0916385  1.0620742 -1.0106822 -0.4353414  1.2792511

round( v, 0 )

 [1] -1  0  0  0  1 -1  1 -1 -1 -1  1  1 -1  0  1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -0.7  1.1 -0.6 -0.8 -1.1  1.1  1.1 -1.0 -0.4  1.3

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -0.75  1.07 -0.57 -0.79 -1.10  1.09  1.06 -1.01 -0.44  1.28

floor( v )

 [1] -1 -1  0  0  1 -1  1 -1 -1 -2  1  1 -2 -1  1

ceiling( v )

 [1] -1  0  0  1  1  0  2  0  0 -1  2  2 -1  0  2

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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